Large-margin loss [1]}, also known as L-SoftMax, was one of the first loss functions that extended SoftMax with an angular margin, which was finally employed for face recognition by SphereFace [2]}.
Later, CosFace [3]} extended this loss to use cosine similarity instead of angular losses and, finally, ArcFace [4]} introduced an additive angular margin to both maximize intra-class similarity and inter-class diversity.
The big advantage of this margin is that it allows some similarity between faces of different people and does not force all of them to be as dissimilar as possible.
Latest loss functions include AdaCos [5]}, PS2Grad [6]}, ring loss [7]}, and MagFace [8]}.
Also, loss functions that explicitly tackle the issue of open-set face recognition have been proposed [9]}, but those techniques will not be discussed further in our study.
Other losses that are worth mentioning are Triplet [10]} and Contrastive Loss [11]}, which are metric learning approaches that work directly on the embedding space by explicitly minimizing within-class and maximizing between-class variability.
